1. Introduction
The rapid growth of e-commerce platforms and cross-border purchasing (daigou) websites has generated vast amounts of user data, which holds significant potential for improving marketing strategies. By integrating and analyzing this data in spreadsheets, businesses can construct detailed user profiles (or user personas) to enable precision marketing campaigns.
2. User Data Integration in Spreadsheets
Key data sources include:
- Basic Information:
- Consumption Behavior:
- Interest Preferences:
- Consumption Behavior:
Spreadsheet tools like Google SheetsExcel
3. Building User Profiles with Data Mining Techniques
3.1 Feature Engineering
Transform raw data into actionable metrics:
=RFM_SCORE(Recency, Frequency, MonetaryValue)
- Recency:
- Frequency:
- Monetary Value:
- Frequency:
3.2 Machine Learning Applications
Embedded scripting (e.g., Google Apps Script or Python integration) can:
- Cluster users via k-means
- Predict preferences using collaborative filtering.
- Generate label-based rules:
IF(LoyaltyScore>80, "VIP", "Standard").
4. Precision Marketing Strategies
| User Segment | Actionable Strategy |
|---|---|
| "Bargain Hunters" | Target with limited-time discounts. |
| "Luxury Shoppers" | Recommend high-end products. |
Case Study:25% increase
5. Conclusion
Combining spreadsheet-based data aggregation with analytical modeling enables businesses to transition from broad campaigns to hyper-targeted marketing, enhancing both user experience and ROI.